计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 51-58.doi: 10.11896/jsjkx.200900194
纪南巡, 孙晓燕, 李祯其
JI Nan-xun, SUN Xiao-yan, LI Zhen-qi
摘要： 随着移动网络和APPs的发展,包含用户评价、标记、打分、图像和视频等多源异构数据的用户生成内容(User Generated Contents,UGC)成为提高个性化服务质量的重要依据,对这些数据的融合和表示学习成为其应用的关键。对此,提出一种面向多源文本和图像的融合表示学习。采用Doc2vec和LDA模型,给出多源文本的向量化表示,采用深度卷积网络获取与评价文本相关的图像特征;给出多源文本向量化表示的多策略融合机制,以及文本和图像卷积融合的表示学习。将所提算法应用于亚马逊含UGC内容的商品数据集上,基于UGC向量化表示物品的分类准确率说明了该算法的可行性和有效性。
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